Professional Data Science and AI Course
Comprehensive Syllabus
Module No. | Topic | Subtopics |
1 | Foundations of Data Science & AI | Introduction, Lifecycle, Ecosystem, Tools, Use Cases |
2 | Programming for Data Science | Python Basics, OOPs, NumPy, Pandas, Matplotlib, Seaborn |
3 | Mathematics for Data Science & AI | Linear Algebra, Calculus, Probability & Statistics |
4 | Data Handling & Preprocessing | Cleaning, Missing Data, Feature Engineering, Encoding, Scaling |
5 | Databases & Data Management | SQL, NoSQL |
6 | Exploratory Data Analysis (EDA) | Univariate/Bivariate Analysis, Correlations, Outliers, Profiling Reports |
7 | Machine Learning | Supervised & Unsupervised Learning, Model Evaluation, Tuning, scikit-learn (sk learn) |
8 | Deep Learning | Neural Networks, CNN, RNN, LSTM, PyTorch and Keras |
9 | Natural Language Processing (NLP) | Tokenization, TF-IDF, Word2Vec, Transformers, BERT, GPT, Text Classification |
10 | Computer Vision | Autoencoders, Segmentation and Object Detection |
11 | Generative AI – I | VAE, GANs (Vanilla, DCGAN), Diffusion Models, Image Generation (un-conditional and conditional) |
12 | Generative AI – II | Understanding and building RAGs and AI agents |
13 | Data Visualization & BI Tools | Tableau, Power BI |
14 | Big Data & Distributed Systems | Hadoop, PySpark |
15 | Capstone Project | Real-World Projects |
